Development and Evaluation of Perl-Based Algorithms to Classify Neoplasms From Pathology Records in Synoptic Report Format

Rossi, Kristen; Echeverria, Diana; Carroll, Anna M; Luse, Tina; Rennix, Christopher

doi:10.1200/cci.20.00152

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Transcriptome expression data and corresponding clinical information of THCA were downloaded from TCGA database, [ 10 ] and a total of 567 samples were collected, including 509 THCA tissue samples and 58 normal paracancer tissue samples. We then used the Perl language [ 11 ] to organize and clean all data for subsequent bioinformatics and statistical analyses.…”

Section: Methodsmentioning

confidence: 99%

Construction and validation of a novel prognostic model for thyroid cancer based on N7-methylguanosine modification-related lncRNAs

et al. 2022

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Background: To construct and verify a novel prognostic model for thyroid cancer (THCA) based on N7-methylguanosine modification-related lncRNAs (m7G-lncRNAs) and their association with immune cell infiltration. Methods: In this study, we identified m7G-lncRNAs using co-expression analysis and performed differential expression analysis of m7G-lncRNAs between groups. We then constructed a THCA prognostic model, performed survival analysis and risk assessment for the THCA prognostic model, and performed independent prognostic analysis and receiver operating characteristic curve analyses to evaluate and validate the prognostic value of the model. Furthermore, analysis of the regulatory relationship between prognostic differentially expressed m7G-related lncRNAs (PDEm7G-lncRNAs) and mRNAs and correlation analysis of immune cells and risk scores in THCA patients were carried out. Results: We identified 29 N7-methylguanosine modification-related mRNAs and 116 differentially expressed m7G-related lncRNAs, including 87 downregulated and 29 upregulated lncRNAs. Next, we obtained 8 PDEm7G-lncRNAs. A final optimized model was constructed consisting of 5 PDEm7G-lncRNAs (DOCK9−DT, DPP4–DT, TMEM105, SMG7–AS1 and HMGA2–AS1). Six PDEm7G-lncRNAs (DOCK9–DT, DPP4–DT, HMGA2–AS1, LINC01976, MID1IP1–AS1, and SMG7–AS1) had positive regulatory relationships with 10 PDEm7G-mRNAs, while 2 PDEm7G-lncRNAs (LINC02026 and TMEM105) had negative regulatory relationships with 2 PDEm7G-mRNAs. Survival curves and risk assessment predicted the prognostic risk in both groups of patients with THCA. Forest maps and receiver operating characteristic curves were used to evaluate and validate the prognostic value of the model. Finally, we demonstrated a correlation between different immune cells and risk scores. Conclusion: Our results will help identify high-risk or low-risk patients with THCA and facilitate early prediction and clinical intervention in patients with high risk and poor prognosis.

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Section: Methodsmentioning

confidence: 99%

Construction and validation of a novel prognostic model for thyroid cancer based on N7-methylguanosine modification-related lncRNAs

et al. 2022

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“…The microRNA le was obtained from the miRcode database (http://www.mircode.org/), and perl software [10] was used to associate the differentially expressed genes with miRNAs. Predictions were made for up-regulated lncRNA-miRNA-mRNA and down-regulated lncRNA-mRNA-miRNA associations.…”

Section: Prediction Of Lncrna-bound Mirnasmentioning

confidence: 99%

Exploring the ceRNA Network of Differentially Expressed lncRNAs in Gout: A Bioinformatics Analysis

Guo

Peng

et al. 2023

Preprint

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Objective: To elucidate the regulatory mechanisms of the competing endogenous RNA (ceRNA) network associated with gout, and to investigate potential targets for diagnosis and treatment of this disease. Methodology: The GSE160170 dataset was downloaded from the GEO database, annotated, and gene attributes were added. Differential expression of lncRNAs and mRNAs between gout patients and non-gout patients was analyzed using R software. The miRNAs targeted by differentially expressed lncRNAs and mRNAs were predicted, and a lncRNA-miRNA-mRNA network was constructed to analyze the competing relationships between lncRNAs and mRNAs. Functional enrichment analysis was performed to investigate the molecular functions of mRNAs regulated by lncRNAs in the ceRNA network. Key mRNAs were identified by constructing protein-protein interaction (PPI) networks and ROC curves, and their diagnostic efficacy was evaluated. Results: Ten differential lncRNAs were identified and analyzed using the ceRNA approach. Functional enrichment analysis showed that the mRNA regulated by differential lncRNAs was significantly enriched in protein phosphatase, DNA transcription factor binding activity, TNF pathway, and toll-like receptor-related pathway. Among the seven mRNAs with high diagnostic value identified through PPI and ROC curve analysis were BTG2, FOS, GATA2, JUN, MAPK6, and NAR4, which were the core genes of this study and have the potential to be used as diagnostic and therapeutic targets for gout. Additionally, five lncRNAs, including FAM182A, UCA1, MIR22HG, TTY10, and FAM215B, affected the expression of key mRNAs by adsorbing miRNAs such as hsa-miR-27a-3p and hsa-miR-1297, which may play a crucial role in the pathogenesis of gout.

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Identification of Novel m6A-Related Long Non-Coding RNA Signatures for Cholangiocarcinoma Using Integrated Bioinformatics Analyses

Jin

Liu

2022

j biomed nanotechnol

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Traditional methods used to treat cholangiocarcinoma are less effective, and the identification of new CHOL signature genes can help in the early clinical diagnosis and intervention of cholangiocarcinoma. In this work, we used integrated bioinformatics analysis to find new m6a-associated lncRNA signatures in cholangiocarcinoma. Pearson correlation test was used to identify m6A-lncRNAs by co-expression analysis of m6A-mrna and lncRNAs. we then selected m6A-lncRNAs co-expressed with METTL3 and METTL14 genes and screened for DEm6A-lncRNAs by comparing expression differences. we then used R package of Spearman coefficient correlation analysis to investigate the relevance of m6A-lncrna expression in CHOL. To determine the relative levels of immune cell infiltration, we performed ssGSEA analysis on all samples using the R package, and then we used graphs to illustrate the differences in immune cell infiltration between the CHOL and NC groups. The results of this study will help to identify new CHOL-causing biosignatures, which are important for the early clinical detection and management of CHOL.

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Development and Evaluation of Perl-Based Algorithms to Classify Neoplasms From Pathology Records in Synoptic Report Format

Cited by 4 publications

References 9 publications

Construction and validation of a novel prognostic model for thyroid cancer based on N7-methylguanosine modification-related lncRNAs

Construction and validation of a novel prognostic model for thyroid cancer based on N7-methylguanosine modification-related lncRNAs

Exploring the ceRNA Network of Differentially Expressed lncRNAs in Gout: A Bioinformatics Analysis

Identification of Novel m6A-Related Long Non-Coding RNA Signatures for Cholangiocarcinoma Using Integrated Bioinformatics Analyses

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